Judgment Preservation in Augmented Intelligence Systems

A Governance Model for Delegated Artificial Intelligence Action

Abstract

performance problem: under what conditions may system capability be translated into actionable authority without eroding human responsibility, contestability, and learning? This problem is important because socio-technical failures frequently arise not only from model error, but also from weak authorization design, ambiguous accountability, poor escalation pathways, and the thinning of meaningful human oversight. It is well suited to systems science because it involves interacting human and technical actors, feedback loops, boundary conditions, incentive structures, and cross-level effects that cannot be adequately understood in isolation. This paper uses a systems-governance methodology that combines conceptual analysis, socio-technical systems modeling, boundary analysis, and failure-mode-oriented institutional design to distinguish capability from authority and to identify conditions under which delegated Artificial Intelligence action remains governable. The analysis develops a four-part governance model centered on bounded capability envelopes, execution-boundary revalidation at the point of action, competence-based human oversight, and observability structures that support escalation, review, and post-incident learning. The result is a framework for preserving meaningful human interpretive, supervisory, and justificatory agency under conditions of increasing machine mediation. The paper contributes to systems science by offering a transferable governance architecture for complex institutional settings and contributes to practice by clarifying how augmented intelligence can support action without surrendering responsibility in sectors such as public administration, education, health, and other regulated decision environments.